Experimental studies of scalability in clustered web systems
Bibliographic record
Abstract
Summary form only given. As the Internet is becoming more robust with the addition of new services such as audio and video streaming, e-commerce, and news casting, the traditional Web server architecture is unable to keep up with the increasing demands and requisites of such new services. This situation has created a need for secure, reliable, highly available, and scalable Web servers that rely on clustering technologies to be able to meet the growth in the user base and offered services. Our work is concerned with scalability and performance of clustered Web systems based on open technologies. We study the scalability of clustered Web servers and HTTP traffic distribution methods through an experimental testbed and present the results of a series of experiments we conducted to benchmark the performance and scalability of a clustered Web system. We describe the prototyped target cluster and its components; the benchmarking methodology, metrics, and test scenarios; and the performance and scalability test results. The results demonstrate nonlinear scalability. We use these results to better understand scalability and performance issues in clustered systems. In future work, we aim to design and build linearly scalable Web server platforms within the concept of next generation Internet server.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".